Results 221 to 230 of about 10,590 (247)

Central Mean Subspace in Time Series

Journal of Computational and Graphical Statistics, 2009
We propose a notion of central mean dimension reduction subspace for time series {xt} which does not require specification of a model but seeks to find a p×d matrix Φd, d≤p, so that the d×1 vector ΦdTXt−1, where Xt−1=(xt−1, …, xt−p)T for some p≥1, includes all the information about xt that is available from E(xt|Xt−1).
Xiangrong Yin, Jin-Hong Park
exaly   +2 more sources

A central limit theorem for subspace algorithms

1997 European Control Conference (ECC), 1997
In the last few years, the so called ‘subspace-algorithms’ have become a quite popular tool for the estimation of linear dynamic systems. However their statistical properties are not fully clarified right now. Earlier papers investigated the consistency of the method. This paper presents a central limit theorem for the estimates.
Dietmar Bauer, W Scherrer
exaly   +3 more sources

Feature filter for estimating central mean subspace and its sparse solution

Computational Statistics and Data Analysis, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Richard Kryscio
exaly   +2 more sources

A Shrinkage Estimation of Central Subspace in Sufficient Dimension Reduction

Communications in Statistics Part B: Simulation and Computation, 2010
Sliced regression is an effective dimension reduction method by replacing the original high-dimensional predictors with its appropriate low-dimensional projection. It is free from any probabilistic assumption and can exhaustively estimate the central subspace.
Qin Wang
exaly   +2 more sources

Central Subspace Dimensionality Reduction Using Covariance Operators

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
We consider the task of dimensionality reduction informed by real-valued multivariate labels. The problem is often treated as Dimensionality Reduction for Regression (DRR), whose goal is to find a low-dimensional representation, the central subspace, of the input data that preserves the statistical correlation with the targets.
Vladimir Pavlovic, Minyoung Kim
exaly   +3 more sources

Estimation and inference on central mean subspace for multivariate response data

Computational Statistics and Data Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liping Zhu
exaly   +2 more sources

Analysing nonlinear time series with central subspace

Journal of Statistical Computation and Simulation, 2012
Traditionally, time series analysis involves building an appropriate model and using either parametric or nonparametric methods to make inference about the model parameters. Motivated by recent developments for dimension reduction in time series, an empirical application of sufficient dimension reduction (SDR) to nonlinear time series modelling is ...
Jin-Hong Park
exaly   +2 more sources

Fused clustering mean estimation of central subspace

Journal of the Korean Statistical Society, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jae Keun Yoo, Yoo Jae Keun
exaly   +3 more sources

Subspace Controllability of Quantum Ising Spin Networks with a Central Spin

2019 18th European Control Conference (ECC), 2019
We consider a class of spin networks where each spin in a certain set interacts via Ising coupling with a single central spin. This is a common situation for instance in NV centers in diamonds. Due to the permutation symmetries of the network, the system is not globally controllable but it displays invariant subspaces of the underlying Hilbert space ...
Domenico D'Alessandro, Francesca Albertini   +1 more
exaly   +4 more sources

Partial central subspace and sliced average variance estimation

Journal of Statistical Planning and Inference, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sanford Weisberg, R Dennis Cook
exaly   +3 more sources